Pen-enabled devices such as tablet pc's and personal digital assistants often use one or more types of handwriting recognizers to allow users to enter data using the pen. Handwriting recognizers analyze the user's handwriting according to a series of classifiers to determine the most likely match. It is often difficult to achieve good handwriting recognition results for cursive handwriting due to the large number of inter and intra person variations (or writing styles) to write the same character. For example, a N stroke character can be written in 1−N strokes (potentially yielding 2^N writings), and the way strokes are connected can vary drastically from person to person and from character to character. In addition, East Asian languages usually have the order of 10,000 characters (codepoints, or classes), which further complicates the problem. Difficulty can also arise from the uneven data distribution (such as when there are much more print training samples than cursive samples), which results in a limited number of cursive samples being present in prototype databases as compared to the number of print samples. Another difficulty can arise when using neural networks for character recognition. Neural networks usually do not scale well to thousands of classes or to characters. Trying to support all these characters in neural network recognizers can drastically degrade system accuracy.